TPR-Attention for Combinatorial Generalization

Systematic generalization remains a significant challenge in deep learning. In particular, combinatorial generalization – generalizing to new configurations of known factors of variation – is effortless for humans but difficult for standard neural architectures that rely on statistical correlations rather than explicit structural representations. We introduce a new architectural component that embeds structured inductive bias into deep learning: an attention mechanism operating over tensor‑product representations (TPRs). Through controlled experiments on compositional tasks, we show that this TPR‑attention mechanism outperforms existing architectural components in combinatorial generalization. These results highlight the value of integrating explicit compositional structure into neural attention and point toward a promising path for models capable of systematic generalization.